DBMS > GridGain vs. Hazelcast vs. InfinityDB vs. MySQL vs. NuoDB

System Properties Comparison GridGain vs. Hazelcast vs. InfinityDB vs. MySQL vs. NuoDB

Editorial information provided by DB-Engines

Name

Description

GridGain is an in-memory computing platform, built on Apache Ignite

A widely adopted in-memory data grid

A Java embedded Key-Value Store which extends the Java Map interface

Widely used open source RDBMS

NuoDB is a webscale distributed database that supports SQL and ACID transactions

Primary database model

Key-value store
Relational DBMS

Key-value store

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

Website

www.3ds.com/nuodb-distributed-sql-database

Technical documentation

www.gridgain.com/docs/index.html

hazelcast.org/imdg/docs

boilerbay.com/infinitydb/manual

dev.mysql.com/doc

doc.nuodb.com

Developer

GridGain Systems, Inc.

Hazelcast

Boiler Bay Inc.

Oracle

since 2010, originally MySQL AB, then Sun

Dassault Systèmes

originally NuoDB, Inc.

Initial release

2007

2008

2002

1995

2013

Current release

GridGain 8.5.1

5.3.6, November 2023

4.0

8.4.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

limited edition free

Cloud-based only

Only available as a cloud service

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Implementation language

Java, C++, .Net

Java

C and C++

C++

Server operating systems

Linux
OS X
Solaris
Windows

All OS with a Java VM

FreeBSD
Linux
OS X
Solaris
Windows

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Data scheme

yes

schema-free

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

the object must implement a serialization strategy

yes

Secondary indexes

yes

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

SQL-like query language

yes

with proprietary extensions

yes

APIs and other access methods

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JCache
JPA
Memcached protocol
RESTful HTTP API

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

ADO.NET
JDBC
ODBC
Proprietary native API

ADO.NET
JDBC
ODBC

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

Java

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.Net
C
C++
Go
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

yes

Event Listeners, Executor Services

yes

proprietary syntax

Java, SQL

Triggers

yes (cache interceptors and events)

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

data is dynamically stored/cached on the nodes where it is read/written

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes

Replicated Map

none

Multi-source replication
Source-replica replication

yes

Managed transparently by NuoDB

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate Consistency

Foreign keys

Referential integrity

manual creation possible, using inversions based on multi-value capability

yes

not for MyISAM storage engine

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

one or two-phase-commit; repeatable reads; read commited

ACID

Optimistic locking for transactions; no isolation for bulk loads

ACID

not for MyISAM storage engine

ACID

tunable commit protocol

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

MVCC

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Temporary table

User concepts

Access control

Security Hooks for custom implementations

Role-based access control

Users with fine-grained authorization concept

no user groups or roles

Standard SQL roles/ privileges, Administrative Users

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